Fine-tuning a vision-language model builds on the same core idea as text-only fine-tuning, but the image half of the pipeline introduces real differences in data, compute and evaluation.
What's different
- Training examples pair an image with text (a caption, a question-answer pair), and image preprocessing (resizing, normalisation) has to be consistent across the dataset.
- VRAM needs are higher than an equivalent text-only fine-tune, since image encoders add real memory overhead on top of the language model.
- Evaluation needs image-grounded test cases specifically — a model can score well on text-only eval questions while still misreading images.
A realistic starting point
LoRA-style efficient fine-tuning applies here too, usually targeting the language-model half of the architecture while keeping the vision encoder frozen — full fine-tuning of both halves at once needs meaningfully more compute.
See multimodal LLMs for student projects.
— Pranjul Rathour, GenAI Engineer from Kanpur, India. Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at any campus: pranjulrathour41@gmail.com.